In 2010, Drew Conway gave us this - our somewhat reductionist definition of a data unicorn

The OG Data Science Cheat Sheet

Analyzing the Analyzers, 2012-2013

Skills distilled from our survey

• Algorithms (ex: computational complexity, CS theory)
• Back-End Programming (ex: JAVA/Rails/Objective C)
• Bayesian/Monte-Carlo Statistics (ex: MCMC, BUGS)
• Big and Distributed Data (ex: Hadoop, Map/Reduce)
• Business (ex: management, business development, budgeting)
• Classical Statistics (ex: general linear model, ANOVA)
• Data Manipulation (ex: regexes, R, SAS, web scraping)
• Front-End Programming (ex: JavaScript, HTML, CSS)
• Graphical Models (ex: social networks, Bayes networks)
• Machine Learning (ex: decision trees, neural nets, SVM, clustering)
• Math (ex: linear algebra, real analysis, calculus)
• Optimization (ex: linear, integer, convex, global)
• Product Development (ex: design, project management)
• Science (ex: experimental design, technical writing/publishing)
• Simulation (ex: discrete, agent-based, continuous)
• Spatial Statistics (ex: geographic covariates, GIS)
• Structured Data (ex: SQL, JSON, XML)
• Surveys and Marketing (ex: multinomial modeling)
• Systems Administration (ex: *nix, DBA, cloud tech.)
• Temporal Statistics (ex: forecasting, time-series analysis)
• Unstructured Data (ex: noSQL, text mining)
• Visualization (ex: statistical graphics, mapping, web-based dataviz)

Evolution of Data Science

timeline graphic (to-be-created)

In 2024, the hard truth: an overloaded definition and set of expectations leading to the Data Practitioner Soup

How it started, how it’s going

The complexities of defining data science

(expand on this)

• science
• research paradigm
• research method
• discipline
• workflow
• profession

Mastering the broadness is extremely difficult and needs time

And teaching is even harder!

Teaching stories